Logo

Ankit Yadav

📊 Loan Default Prediction Report | Generated on 2025-04-26 16:47:30

Overview

Brought to you by YData

Dataset statistics

Number of variables13
Number of observations5960
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.1 MiB
Average record size in memory198.6 B

Variable types

Categorical5
Numeric8

Alerts

DEROG has constant value "0.0" Constant
DELINQ has constant value "0.0" Constant
MORTDUE is highly overall correlated with VALUEHigh correlation
VALUE is highly overall correlated with MORTDUEHigh correlation
YOJ has 415 (7.0%) zeros Zeros
NINQ has 2531 (42.5%) zeros Zeros
CLNO has 62 (1.0%) zeros Zeros

Reproduction

Analysis started2025-04-26 11:17:31.199044
Analysis finished2025-04-26 11:17:42.304867
Duration11.11 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

BAD
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size291.1 KiB
0
4771 
1
1189 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5960
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 4771
80.1%
1 1189
 
19.9%

Length

2025-04-26T16:47:42.490141image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-26T16:47:42.568409image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 4771
80.1%
1 1189
 
19.9%

Most occurring characters

ValueCountFrequency (%)
0 4771
80.1%
1 1189
 
19.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5960
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4771
80.1%
1 1189
 
19.9%

Most occurring scripts

ValueCountFrequency (%)
Common 5960
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4771
80.1%
1 1189
 
19.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5960
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4771
80.1%
1 1189
 
19.9%

LOAN
Real number (ℝ)

Distinct390
Distinct (%)6.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18051.896
Minimum1100
Maximum41600
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.7 KiB
2025-04-26T16:47:42.680001image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1100
5-th percentile5900
Q111100
median16300
Q323300
95-th percentile40000
Maximum41600
Range40500
Interquartile range (IQR)12200

Descriptive statistics

Standard deviation9252.5653
Coefficient of variation (CV)0.51255366
Kurtosis0.27308773
Mean18051.896
Median Absolute Deviation (MAD)6000
Skewness0.83238935
Sum1.075893 × 108
Variance85609965
MonotonicityIncreasing
2025-04-26T16:47:43.030088image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
41600 260
 
4.4%
15000 105
 
1.8%
10000 81
 
1.4%
20000 74
 
1.2%
25000 73
 
1.2%
12000 69
 
1.2%
17000 51
 
0.9%
5000 50
 
0.8%
13000 50
 
0.8%
11000 47
 
0.8%
Other values (380) 5100
85.6%
ValueCountFrequency (%)
1100 1
 
< 0.1%
1300 1
 
< 0.1%
1500 2
 
< 0.1%
1700 2
 
< 0.1%
1800 2
 
< 0.1%
2000 6
0.1%
2100 1
 
< 0.1%
2200 3
0.1%
2300 3
0.1%
2400 6
0.1%
ValueCountFrequency (%)
41600 260
4.4%
41500 1
 
< 0.1%
41400 2
 
< 0.1%
41300 1
 
< 0.1%
41200 3
 
0.1%
41100 3
 
0.1%
40900 5
 
0.1%
40800 2
 
< 0.1%
40700 3
 
0.1%
40600 3
 
0.1%

MORTDUE
Real number (ℝ)

High correlation 

Distinct4755
Distinct (%)79.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean70517.314
Minimum2063
Maximum148292.12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.7 KiB
2025-04-26T16:47:43.209319image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2063
5-th percentile19105.45
Q148139
median65019
Q388200.25
95-th percentile148292.12
Maximum148292.12
Range146229.12
Interquartile range (IQR)40061.25

Descriptive statistics

Standard deviation34463.827
Coefficient of variation (CV)0.48872859
Kurtosis-0.04794925
Mean70517.314
Median Absolute Deviation (MAD)19129
Skewness0.61659244
Sum4.2028319 × 108
Variance1.1877554 × 109
MonotonicityNot monotonic
2025-04-26T16:47:43.391906image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
65019 518
 
8.7%
148292.125 308
 
5.2%
42000 11
 
0.2%
47000 10
 
0.2%
65000 9
 
0.2%
50000 7
 
0.1%
124000 7
 
0.1%
55000 7
 
0.1%
62000 7
 
0.1%
45000 7
 
0.1%
Other values (4745) 5069
85.1%
ValueCountFrequency (%)
2063 1
< 0.1%
2619 1
< 0.1%
2800 1
< 0.1%
3372 1
< 0.1%
4000 1
< 0.1%
4447 1
< 0.1%
4500 1
< 0.1%
4641 1
< 0.1%
4734 1
< 0.1%
4742 1
< 0.1%
ValueCountFrequency (%)
148292.125 308
5.2%
148235 1
 
< 0.1%
147855 1
 
< 0.1%
147801 1
 
< 0.1%
147696 1
 
< 0.1%
147638 1
 
< 0.1%
147622 1
 
< 0.1%
147600 1
 
< 0.1%
147577 1
 
< 0.1%
147522 1
 
< 0.1%

VALUE
Real number (ℝ)

High correlation 

Distinct5048
Distinct (%)84.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean98212.468
Minimum8000
Maximum197777.62
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.7 KiB
2025-04-26T16:47:43.544240image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum8000
5-th percentile39260.8
Q166489.5
median89235.5
Q3119004.75
95-th percentile197777.62
Maximum197777.62
Range189777.62
Interquartile range (IQR)52515.25

Descriptive statistics

Standard deviation44321.429
Coefficient of variation (CV)0.45128109
Kurtosis-0.10474873
Mean98212.468
Median Absolute Deviation (MAD)25427
Skewness0.77114258
Sum5.8534631 × 108
Variance1.9643891 × 109
MonotonicityNot monotonic
2025-04-26T16:47:43.766955image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
197777.625 347
 
5.8%
89235.5 112
 
1.9%
60000 15
 
0.3%
80000 14
 
0.2%
85000 12
 
0.2%
65000 11
 
0.2%
78000 10
 
0.2%
72000 9
 
0.2%
50000 8
 
0.1%
68000 8
 
0.1%
Other values (5038) 5414
90.8%
ValueCountFrequency (%)
8000 1
< 0.1%
8800 1
< 0.1%
9100 1
< 0.1%
9500 1
< 0.1%
11550 1
< 0.1%
11702 1
< 0.1%
12414 1
< 0.1%
12500 1
< 0.1%
12737 1
< 0.1%
12972 1
< 0.1%
ValueCountFrequency (%)
197777.625 347
5.8%
197722 1
 
< 0.1%
197634 1
 
< 0.1%
197592 1
 
< 0.1%
197500 1
 
< 0.1%
196968 1
 
< 0.1%
196860 1
 
< 0.1%
196555 1
 
< 0.1%
196335 1
 
< 0.1%
196270 1
 
< 0.1%

REASON
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size327.5 KiB
DebtCon
3928 
HomeImp
1780 
Not Specified
 
252

Length

Max length13
Median length7
Mean length7.2536913
Min length7

Characters and Unicode

Total characters43232
Distinct characters18
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHomeImp
2nd rowHomeImp
3rd rowHomeImp
4th rowNot Specified
5th rowHomeImp

Common Values

ValueCountFrequency (%)
DebtCon 3928
65.9%
HomeImp 1780
29.9%
Not Specified 252
 
4.2%

Length

2025-04-26T16:47:43.913027image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-26T16:47:43.989236image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
debtcon 3928
63.2%
homeimp 1780
28.7%
not 252
 
4.1%
specified 252
 
4.1%

Most occurring characters

ValueCountFrequency (%)
e 6212
14.4%
o 5960
13.8%
t 4180
9.7%
D 3928
9.1%
b 3928
9.1%
C 3928
9.1%
n 3928
9.1%
m 3560
8.2%
p 2032
 
4.7%
I 1780
 
4.1%
Other values (8) 3796
8.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 31060
71.8%
Uppercase Letter 11920
 
27.6%
Space Separator 252
 
0.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 6212
20.0%
o 5960
19.2%
t 4180
13.5%
b 3928
12.6%
n 3928
12.6%
m 3560
11.5%
p 2032
 
6.5%
i 504
 
1.6%
c 252
 
0.8%
f 252
 
0.8%
Uppercase Letter
ValueCountFrequency (%)
D 3928
33.0%
C 3928
33.0%
I 1780
14.9%
H 1780
14.9%
N 252
 
2.1%
S 252
 
2.1%
Space Separator
ValueCountFrequency (%)
252
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 42980
99.4%
Common 252
 
0.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 6212
14.5%
o 5960
13.9%
t 4180
9.7%
D 3928
9.1%
b 3928
9.1%
C 3928
9.1%
n 3928
9.1%
m 3560
8.3%
p 2032
 
4.7%
I 1780
 
4.1%
Other values (7) 3544
8.2%
Common
ValueCountFrequency (%)
252
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 43232
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 6212
14.4%
o 5960
13.8%
t 4180
9.7%
D 3928
9.1%
b 3928
9.1%
C 3928
9.1%
n 3928
9.1%
m 3560
8.2%
p 2032
 
4.7%
I 1780
 
4.1%
Other values (8) 3796
8.8%

JOB
Categorical

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size316.4 KiB
Other
2388 
ProfExe
1276 
Office
948 
Mgr
767 
No Job
279 
Other values (2)
302 

Length

Max length7
Median length6
Mean length5.3442953
Min length3

Characters and Unicode

Total characters31852
Distinct characters22
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOther
2nd rowOther
3rd rowOther
4th rowNo Job
5th rowOffice

Common Values

ValueCountFrequency (%)
Other 2388
40.1%
ProfExe 1276
21.4%
Office 948
 
15.9%
Mgr 767
 
12.9%
No Job 279
 
4.7%
Self 193
 
3.2%
Sales 109
 
1.8%

Length

2025-04-26T16:47:44.096794image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-26T16:47:44.217469image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
other 2388
38.3%
profexe 1276
20.5%
office 948
 
15.2%
mgr 767
 
12.3%
no 279
 
4.5%
job 279
 
4.5%
self 193
 
3.1%
sales 109
 
1.7%

Most occurring characters

ValueCountFrequency (%)
e 4914
15.4%
r 4431
13.9%
f 3365
10.6%
O 3336
10.5%
t 2388
7.5%
h 2388
7.5%
o 1834
 
5.8%
P 1276
 
4.0%
E 1276
 
4.0%
x 1276
 
4.0%
Other values (12) 5368
16.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 24058
75.5%
Uppercase Letter 7515
 
23.6%
Space Separator 279
 
0.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 4914
20.4%
r 4431
18.4%
f 3365
14.0%
t 2388
9.9%
h 2388
9.9%
o 1834
 
7.6%
x 1276
 
5.3%
c 948
 
3.9%
i 948
 
3.9%
g 767
 
3.2%
Other values (4) 799
 
3.3%
Uppercase Letter
ValueCountFrequency (%)
O 3336
44.4%
P 1276
 
17.0%
E 1276
 
17.0%
M 767
 
10.2%
S 302
 
4.0%
N 279
 
3.7%
J 279
 
3.7%
Space Separator
ValueCountFrequency (%)
279
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 31573
99.1%
Common 279
 
0.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 4914
15.6%
r 4431
14.0%
f 3365
10.7%
O 3336
10.6%
t 2388
7.6%
h 2388
7.6%
o 1834
 
5.8%
P 1276
 
4.0%
E 1276
 
4.0%
x 1276
 
4.0%
Other values (11) 5089
16.1%
Common
ValueCountFrequency (%)
279
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 31852
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 4914
15.4%
r 4431
13.9%
f 3365
10.6%
O 3336
10.5%
t 2388
7.5%
h 2388
7.5%
o 1834
 
5.8%
P 1276
 
4.0%
E 1276
 
4.0%
x 1276
 
4.0%
Other values (12) 5368
16.9%

YOJ
Real number (ℝ)

Zeros 

Distinct86
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.6451091
Minimum0
Maximum25.5
Zeros415
Zeros (%)7.0%
Negative0
Negative (%)0.0%
Memory size46.7 KiB
2025-04-26T16:47:44.415099image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median7
Q312
95-th percentile24
Maximum25.5
Range25.5
Interquartile range (IQR)9

Descriptive statistics

Standard deviation6.9532653
Coefficient of variation (CV)0.80430048
Kurtosis0.00056679553
Mean8.6451091
Median Absolute Deviation (MAD)4
Skewness0.90561204
Sum51524.85
Variance48.347899
MonotonicityNot monotonic
2025-04-26T16:47:44.656259image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7 759
 
12.7%
0 415
 
7.0%
1 363
 
6.1%
2 347
 
5.8%
5 333
 
5.6%
4 324
 
5.4%
6 318
 
5.3%
3 307
 
5.2%
9 286
 
4.8%
10 285
 
4.8%
Other values (76) 2223
37.3%
ValueCountFrequency (%)
0 415
7.0%
0.1 14
 
0.2%
0.2 10
 
0.2%
0.25 1
 
< 0.1%
0.3 7
 
0.1%
0.4 9
 
0.2%
0.5 7
 
0.1%
0.6 4
 
0.1%
0.7 4
 
0.1%
0.75 2
 
< 0.1%
ValueCountFrequency (%)
25.5 211
3.5%
25 56
 
0.9%
24.5 1
 
< 0.1%
24 75
 
1.3%
23 77
 
1.3%
22.8 1
 
< 0.1%
22 67
 
1.1%
21 69
 
1.2%
20 82
 
1.4%
19.5 1
 
< 0.1%

DEROG
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size302.8 KiB
0.0
5960 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters17880
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 5960
100.0%

Length

2025-04-26T16:47:44.816369image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-26T16:47:44.901821image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 5960
100.0%

Most occurring characters

ValueCountFrequency (%)
0 11920
66.7%
. 5960
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 11920
66.7%
Other Punctuation 5960
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 11920
100.0%
Other Punctuation
ValueCountFrequency (%)
. 5960
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 17880
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 11920
66.7%
. 5960
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 17880
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 11920
66.7%
. 5960
33.3%

DELINQ
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size302.8 KiB
0.0
5960 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters17880
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 5960
100.0%

Length

2025-04-26T16:47:44.967974image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-26T16:47:45.049133image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 5960
100.0%

Most occurring characters

ValueCountFrequency (%)
0 11920
66.7%
. 5960
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 11920
66.7%
Other Punctuation 5960
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 11920
100.0%
Other Punctuation
ValueCountFrequency (%)
. 5960
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 17880
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 11920
66.7%
. 5960
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 17880
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 11920
66.7%
. 5960
33.3%

CLAGE
Real number (ℝ)

Distinct5249
Distinct (%)88.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean178.22661
Minimum0
Maximum391.8005
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size46.7 KiB
2025-04-26T16:47:45.137778image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile69.644236
Q1117.37143
median173.46667
Q3227.14306
95-th percentile319.65841
Maximum391.8005
Range391.8005
Interquartile range (IQR)109.77163

Descriptive statistics

Standard deviation77.995604
Coefficient of variation (CV)0.43762043
Kurtosis-0.22653164
Mean178.22661
Median Absolute Deviation (MAD)55.293364
Skewness0.48368095
Sum1062230.6
Variance6083.3143
MonotonicityNot monotonic
2025-04-26T16:47:45.307935image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
173.4666667 310
 
5.2%
391.8005003 66
 
1.1%
206.9666667 7
 
0.1%
102.5 7
 
0.1%
95.36666667 6
 
0.1%
123.7666667 6
 
0.1%
109.5666667 6
 
0.1%
177.5 6
 
0.1%
219.1333333 5
 
0.1%
304.3666667 5
 
0.1%
Other values (5239) 5536
92.9%
ValueCountFrequency (%)
0 2
< 0.1%
0.4867114508 1
< 0.1%
0.5071145295 1
< 0.1%
2.033333333 1
< 0.1%
2.820785578 1
< 0.1%
3.04438414 1
< 0.1%
4.412770061 1
< 0.1%
5.243341044 1
< 0.1%
6.133333333 1
< 0.1%
8.055265077 1
< 0.1%
ValueCountFrequency (%)
391.8005003 66
1.1%
385.5 1
 
< 0.1%
384.5 1
 
< 0.1%
384.2850469 1
 
< 0.1%
382.8588359 1
 
< 0.1%
382.312217 1
 
< 0.1%
381.0256475 1
 
< 0.1%
380.2780405 1
 
< 0.1%
379.3499549 1
 
< 0.1%
378.8258185 1
 
< 0.1%

NINQ
Real number (ℝ)

Zeros 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0854027
Minimum0
Maximum5
Zeros2531
Zeros (%)42.5%
Negative0
Negative (%)0.0%
Memory size46.7 KiB
2025-04-26T16:47:45.418017image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile4
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.3128977
Coefficient of variation (CV)1.209595
Kurtosis1.507957
Mean1.0854027
Median Absolute Deviation (MAD)1
Skewness1.4160463
Sum6469
Variance1.7237003
MonotonicityNot monotonic
2025-04-26T16:47:45.516843image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 2531
42.5%
1 1849
31.0%
2 780
 
13.1%
3 392
 
6.6%
5 252
 
4.2%
4 156
 
2.6%
ValueCountFrequency (%)
0 2531
42.5%
1 1849
31.0%
2 780
 
13.1%
3 392
 
6.6%
4 156
 
2.6%
5 252
 
4.2%
ValueCountFrequency (%)
5 252
 
4.2%
4 156
 
2.6%
3 392
 
6.6%
2 780
 
13.1%
1 1849
31.0%
0 2531
42.5%

CLNO
Real number (ℝ)

Zeros 

Distinct44
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.994379
Minimum0
Maximum42.5
Zeros62
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size46.7 KiB
2025-04-26T16:47:45.658085image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7
Q115
median20
Q326
95-th percentile40
Maximum42.5
Range42.5
Interquartile range (IQR)11

Descriptive statistics

Standard deviation9.2451696
Coefficient of variation (CV)0.44036404
Kurtosis-0.061870947
Mean20.994379
Median Absolute Deviation (MAD)6
Skewness0.37976359
Sum125126.5
Variance85.47316
MonotonicityNot monotonic
2025-04-26T16:47:45.806844image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
20 453
 
7.6%
16 316
 
5.3%
19 307
 
5.2%
24 264
 
4.4%
23 259
 
4.3%
21 235
 
3.9%
18 225
 
3.8%
25 221
 
3.7%
42.5 219
 
3.7%
15 217
 
3.6%
Other values (34) 3244
54.4%
ValueCountFrequency (%)
0 62
1.0%
1 6
 
0.1%
2 15
 
0.3%
3 34
 
0.6%
4 42
 
0.7%
5 47
 
0.8%
6 60
1.0%
7 76
1.3%
8 92
1.5%
9 127
2.1%
ValueCountFrequency (%)
42.5 219
3.7%
42 42
 
0.7%
41 24
 
0.4%
40 20
 
0.3%
39 20
 
0.3%
38 39
 
0.7%
37 50
 
0.8%
36 70
 
1.2%
35 78
 
1.3%
34 89
1.5%

DEBTINC
Real number (ℝ)

Distinct4448
Distinct (%)74.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.999967
Minimum19.98306
Maximum48.729991
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.7 KiB
2025-04-26T16:47:46.003427image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum19.98306
5-th percentile21.469701
Q130.763159
median34.818262
Q337.949892
95-th percentile42.325285
Maximum48.729991
Range28.74693
Interquartile range (IQR)7.1867326

Descriptive statistics

Standard deviation5.9536461
Coefficient of variation (CV)0.17510741
Kurtosis0.091355396
Mean33.999967
Median Absolute Deviation (MAD)3.5015052
Skewness-0.53440133
Sum202639.8
Variance35.445901
MonotonicityNot monotonic
2025-04-26T16:47:46.248939image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
34.81826182 1268
 
21.3%
19.98306047 198
 
3.3%
48.72999084 49
 
0.8%
38.14905923 1
 
< 0.1%
39.91928991 1
 
< 0.1%
35.61202481 1
 
< 0.1%
38.18834501 1
 
< 0.1%
44.38257789 1
 
< 0.1%
34.96414103 1
 
< 0.1%
43.64534866 1
 
< 0.1%
Other values (4438) 4438
74.5%
ValueCountFrequency (%)
19.98306047 198
3.3%
20.01032357 1
 
< 0.1%
20.02628441 1
 
< 0.1%
20.0283535 1
 
< 0.1%
20.05952718 1
 
< 0.1%
20.06704205 1
 
< 0.1%
20.07933438 1
 
< 0.1%
20.09078333 1
 
< 0.1%
20.09904978 1
 
< 0.1%
20.11698202 1
 
< 0.1%
ValueCountFrequency (%)
48.72999084 49
0.8%
48.56957404 1
 
< 0.1%
48.34023474 1
 
< 0.1%
48.27759122 1
 
< 0.1%
48.07753147 1
 
< 0.1%
47.91511242 1
 
< 0.1%
47.91452111 1
 
< 0.1%
47.84029512 1
 
< 0.1%
47.83067113 1
 
< 0.1%
47.79590611 1
 
< 0.1%

Interactions

2025-04-26T16:47:40.645845image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-26T16:47:32.395873image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-26T16:47:33.487627image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-26T16:47:34.836018image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-26T16:47:36.046435image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-26T16:47:37.182714image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-26T16:47:38.194272image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-26T16:47:39.428746image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-26T16:47:40.798858image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-26T16:47:32.538550image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-26T16:47:33.753670image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-26T16:47:35.006814image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-26T16:47:36.176520image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-26T16:47:37.341644image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-26T16:47:38.289410image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-26T16:47:39.607831image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-26T16:47:40.955678image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-26T16:47:32.642773image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-26T16:47:33.877644image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-26T16:47:35.173263image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-26T16:47:36.339551image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-26T16:47:37.454105image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-26T16:47:38.482336image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-26T16:47:39.766468image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-26T16:47:41.083478image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-26T16:47:32.787128image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-26T16:47:34.027867image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-26T16:47:35.307017image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-26T16:47:36.513866image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-26T16:47:37.601896image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-26T16:47:38.647001image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-26T16:47:39.924805image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-26T16:47:41.238716image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-26T16:47:32.914704image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-26T16:47:34.186810image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-26T16:47:35.483438image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-26T16:47:36.630691image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-26T16:47:37.733521image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-26T16:47:38.801863image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-26T16:47:40.084269image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-26T16:47:41.419852image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-26T16:47:33.056365image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-26T16:47:34.347977image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-26T16:47:35.613061image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-26T16:47:36.788376image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-26T16:47:37.851813image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-26T16:47:38.967022image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-26T16:47:40.224737image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-26T16:47:41.554375image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-26T16:47:33.174471image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-26T16:47:34.515594image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-26T16:47:35.697639image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-26T16:47:36.933399image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-26T16:47:37.972795image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-26T16:47:39.126620image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-26T16:47:40.372853image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-26T16:47:41.643810image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-26T16:47:33.346388image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-26T16:47:34.658886image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-26T16:47:35.855657image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-26T16:47:37.042879image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-26T16:47:38.086772image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-26T16:47:39.277082image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-26T16:47:40.504876image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-04-26T16:47:46.389289image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
BADCLAGECLNODEBTINCJOBLOANMORTDUENINQREASONVALUEYOJ
BAD1.0000.1830.1170.4720.1310.1850.0860.1780.0330.1250.101
CLAGE0.1831.0000.2330.0120.1470.1190.119-0.0960.1630.1830.159
CLNO0.1170.2331.0000.1590.1580.1410.3190.1400.1370.3550.045
DEBTINC0.4720.0120.1591.0000.0930.0980.1390.1470.1130.133-0.053
JOB0.1310.1470.1580.0931.0000.1100.1730.0980.2840.1750.107
LOAN0.1850.1190.1410.0980.1101.0000.1840.0360.2350.3400.089
MORTDUE0.0860.1190.3190.1390.1730.1841.0000.0520.1230.792-0.050
NINQ0.178-0.0960.1400.1470.0980.0360.0521.0000.1250.003-0.062
REASON0.0330.1630.1370.1130.2840.2350.1230.1251.0000.0750.127
VALUE0.1250.1830.3550.1330.1750.3400.7920.0030.0751.0000.032
YOJ0.1010.1590.045-0.0530.1070.089-0.050-0.0620.1270.0321.000

Missing values

2025-04-26T16:47:41.900918image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-04-26T16:47:42.108567image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

BADLOANMORTDUEVALUEREASONJOBYOJDEROGDELINQCLAGENINQCLNODEBTINC
011100.025860.039025.0HomeImpOther10.50.00.094.3666671.09.034.818262
111300.070053.068400.0HomeImpOther7.00.00.0121.8333330.014.034.818262
211500.013500.016700.0HomeImpOther4.00.00.0149.4666671.010.034.818262
311500.065019.089235.5Not SpecifiedNo Job7.00.00.0173.4666671.020.034.818262
401700.097800.0112000.0HomeImpOffice3.00.00.093.3333330.014.034.818262
511700.030548.040320.0HomeImpOther9.00.00.0101.4660021.08.037.113614
611800.048649.057037.0HomeImpOther5.00.00.077.1000001.017.034.818262
711800.028502.043034.0HomeImpOther11.00.00.088.7660300.08.036.884894
812000.032700.046740.0HomeImpOther3.00.00.0216.9333331.012.034.818262
912000.065019.062250.0HomeImpSales16.00.00.0115.8000000.013.034.818262
BADLOANMORTDUEVALUEREASONJOBYOJDEROGDELINQCLAGENINQCLNODEBTINC
5950041600.055938.086794.0DebtConOther15.00.00.0223.8810400.016.036.753653
5951041600.054004.094838.0DebtConOther16.00.00.0193.7020510.015.036.262691
5952041600.050240.094687.0DebtConOther16.00.00.0214.4262060.016.034.751158
5953041600.053307.094058.0DebtConOther16.00.00.0218.3049780.015.034.242465
5954041600.048919.093371.0DebtConOther15.00.00.0205.6501590.015.034.818262
5955041600.057264.090185.0DebtConOther16.00.00.0221.8087180.016.036.112347
5956041600.054576.092937.0DebtConOther16.00.00.0208.6920700.015.035.859971
5957041600.054045.092924.0DebtConOther15.00.00.0212.2796970.015.035.556590
5958041600.050370.091861.0DebtConOther14.00.00.0213.8927090.016.034.340882
5959041600.048811.088934.0DebtConOther15.00.00.0219.6010020.016.034.571519